{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZA0OfyzXezy1"
      },
      "source": [
        "pytorch 支持 sklearn 的重参数搜索需要其他的库。一般的项目中设置好命令行脚本后，可以通过更改脚本参数手动实现超参数搜索"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:04:08.511766Z",
          "start_time": "2025-01-17T06:03:59.306541Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0P_4KI3ezy7",
        "outputId": "1a5a39ab-dbf2-4167-d962-dde6d5ceb2ea"
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import pandas as pd\n",
        "import os\n",
        "import sys\n",
        "import time\n",
        "from tqdm.auto import tqdm\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "print(sys.version_info)\n",
        "for module in mpl, np, pd, sklearn, torch:\n",
        "    print(module.__name__, module.__version__)\n",
        "\n",
        "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "print(device)\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sys.version_info(major=3, minor=11, micro=11, releaselevel='final', serial=0)\n",
            "matplotlib 3.10.0\n",
            "numpy 1.26.4\n",
            "pandas 2.2.2\n",
            "sklearn 1.6.0\n",
            "torch 2.5.1+cu121\n",
            "cuda:0\n"
          ]
        }
      ],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFlREGeaezzB"
      },
      "source": [
        "## 准备数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:04:24.281781Z",
          "start_time": "2025-01-17T06:04:24.045793Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72fmhGHvezzC",
        "outputId": "f3fa2a54-38a3-4164-e043-15ddada596ce"
      },
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "\n",
        "housing = fetch_california_housing()\n",
        "print(housing.DESCR)\n",
        "print(housing.data.shape)\n",
        "print(housing.target.shape)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _california_housing_dataset:\n",
            "\n",
            "California Housing dataset\n",
            "--------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            ":Number of Instances: 20640\n",
            "\n",
            ":Number of Attributes: 8 numeric, predictive attributes and the target\n",
            "\n",
            ":Attribute Information:\n",
            "    - MedInc        median income in block group\n",
            "    - HouseAge      median house age in block group\n",
            "    - AveRooms      average number of rooms per household\n",
            "    - AveBedrms     average number of bedrooms per household\n",
            "    - Population    block group population\n",
            "    - AveOccup      average number of household members\n",
            "    - Latitude      block group latitude\n",
            "    - Longitude     block group longitude\n",
            "\n",
            ":Missing Attribute Values: None\n",
            "\n",
            "This dataset was obtained from the StatLib repository.\n",
            "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
            "\n",
            "The target variable is the median house value for California districts,\n",
            "expressed in hundreds of thousands of dollars ($100,000).\n",
            "\n",
            "This dataset was derived from the 1990 U.S. census, using one row per census\n",
            "block group. A block group is the smallest geographical unit for which the U.S.\n",
            "Census Bureau publishes sample data (a block group typically has a population\n",
            "of 600 to 3,000 people).\n",
            "\n",
            "A household is a group of people residing within a home. Since the average\n",
            "number of rooms and bedrooms in this dataset are provided per household, these\n",
            "columns may take surprisingly large values for block groups with few households\n",
            "and many empty houses, such as vacation resorts.\n",
            "\n",
            "It can be downloaded/loaded using the\n",
            ":func:`sklearn.datasets.fetch_california_housing` function.\n",
            "\n",
            ".. rubric:: References\n",
            "\n",
            "- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
            "  Statistics and Probability Letters, 33 (1997) 291-297\n",
            "\n",
            "(20640, 8)\n",
            "(20640,)\n"
          ]
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-07-19T03:20:55.356503800Z",
          "start_time": "2024-07-19T03:20:55.349888300Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sduBkMS-ezzF",
        "outputId": "385efd1e-ab49-429f-ba54-456a8e27e436"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,\n",
            "         1.02380952e+00,  3.22000000e+02,  2.55555556e+00,\n",
            "         3.78800000e+01, -1.22230000e+02],\n",
            "       [ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,\n",
            "         9.71880492e-01,  2.40100000e+03,  2.10984183e+00,\n",
            "         3.78600000e+01, -1.22220000e+02],\n",
            "       [ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,\n",
            "         1.07344633e+00,  4.96000000e+02,  2.80225989e+00,\n",
            "         3.78500000e+01, -1.22240000e+02],\n",
            "       [ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,\n",
            "         1.07305936e+00,  5.58000000e+02,  2.54794521e+00,\n",
            "         3.78500000e+01, -1.22250000e+02],\n",
            "       [ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,\n",
            "         1.08108108e+00,  5.65000000e+02,  2.18146718e+00,\n",
            "         3.78500000e+01, -1.22250000e+02]])\n",
            "--------------------------------------------------\n",
            "array([4.526, 3.585, 3.521, 3.413, 3.422])\n"
          ]
        }
      ],
      "source": [
        "# print(housing.data[0:5])\n",
        "import pprint  #打印的格式比较 好看\n",
        "\n",
        "pprint.pprint(housing.data[0:5])\n",
        "print('-'*50)\n",
        "pprint.pprint(housing.target[0:5])"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:04:38.792821Z",
          "start_time": "2025-01-17T06:04:38.590651Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BuRf78uezzG",
        "outputId": "758c4c4f-99ff-4e27-c865-1add086694bf"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#拆分训练集和测试集，random_state是随机种子,同样的随机数种子，是为了得到同样的随机值\n",
        "x_train_all, x_test, y_train_all, y_test = train_test_split(\n",
        "    housing.data, housing.target, random_state = 7)\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(\n",
        "    x_train_all, y_train_all, random_state = 11)\n",
        "# 训练集\n",
        "print(x_train.shape, y_train.shape)\n",
        "# 验证集\n",
        "print(x_valid.shape, y_valid.shape)\n",
        "# 测试集\n",
        "print(x_test.shape, y_test.shape)\n",
        "\n",
        "dataset_maps = {\n",
        "    \"train\": [x_train, y_train],\n",
        "    \"valid\": [x_valid, y_valid],\n",
        "    \"test\": [x_test, y_test],\n",
        "}\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(11610, 8) (11610,)\n",
            "(3870, 8) (3870,)\n",
            "(5160, 8) (5160,)\n"
          ]
        }
      ],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:04:53.931823Z",
          "start_time": "2025-01-17T06:04:53.925971Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "g-aY2UVrezzJ",
        "outputId": "1a60e7f3-de08-4b73-8a02-f597e82e38a7"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(x_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "execution_count": 5
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e66_JRhGezzK"
      },
      "source": [
        "### 构建数据集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:05:14.993923Z",
          "start_time": "2025-01-17T06:05:14.988051Z"
        },
        "id": "0ebDLdV1ezzL"
      },
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class HousingDataset(Dataset):\n",
        "    def __init__(self, mode='train'):\n",
        "        self.x, self.y = dataset_maps[mode]\n",
        "        self.x = torch.from_numpy(scaler.transform(self.x)).float()\n",
        "        self.y = torch.from_numpy(self.y).float().reshape(-1, 1)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.x)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.x[idx], self.y[idx]\n",
        "\n",
        "\n",
        "train_ds = HousingDataset(\"train\")\n",
        "valid_ds = HousingDataset(\"valid\")\n",
        "test_ds = HousingDataset(\"test\")"
      ],
      "outputs": [],
      "execution_count": 6
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-04-25T03:30:34.945253200Z",
          "start_time": "2024-04-25T03:30:34.894354300Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_vU0YASezzM",
        "outputId": "db87179c-ea2e-4785-f853-9d0ce6260ae7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([-0.2981,  0.3523, -0.1092, -0.2506, -0.0341, -0.0060,  1.0806, -1.0611]),\n",
              " tensor([1.5140]))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train_ds[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gQCQKU-ezzN"
      },
      "source": [
        "### DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:05:47.746736Z",
          "start_time": "2025-01-17T06:05:47.743621Z"
        },
        "id": "101DsCSnezzO"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "\n",
        "batch_size = 256 #大一些，可以加快训练速度\n",
        "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=False)\n",
        "test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)"
      ],
      "outputs": [],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1jFceyAezzO"
      },
      "source": [
        "## 定义模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:06:03.640817Z",
          "start_time": "2025-01-17T06:06:03.637447Z"
        },
        "id": "2a9rWhIAezzP"
      },
      "source": [
        "#回归模型我们只需要1个数\n",
        "\n",
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self, input_dim=8):\n",
        "        super().__init__()\n",
        "        self.linear_relu_stack = nn.Sequential(\n",
        "            nn.Linear(input_dim, 30),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(30, 1)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x.shape [batch size, 8]\n",
        "        logits = self.linear_relu_stack(x)\n",
        "        # logits.shape [batch size, 1]\n",
        "        return logits"
      ],
      "outputs": [],
      "execution_count": 9
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:08:55.328460Z",
          "start_time": "2025-01-17T06:08:55.325289Z"
        },
        "id": "YQvbpd-SezzP"
      },
      "source": [
        "class EarlyStopCallback:\n",
        "    def __init__(self, patience=5, min_delta=0.01):\n",
        "        \"\"\"\n",
        "\n",
        "        Args:\n",
        "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
        "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute\n",
        "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
        "        \"\"\"\n",
        "        self.patience = patience\n",
        "        self.min_delta = min_delta\n",
        "        self.best_metric = -1\n",
        "        self.counter = 0\n",
        "\n",
        "    def __call__(self, metric):\n",
        "        if metric >= self.best_metric + self.min_delta:\n",
        "            # update best metric\n",
        "            self.best_metric = metric\n",
        "            # reset counter\n",
        "            self.counter = 0\n",
        "        else:\n",
        "            self.counter += 1\n",
        "\n",
        "    @property\n",
        "    def early_stop(self):\n",
        "        return self.counter >= self.patience\n"
      ],
      "outputs": [],
      "execution_count": 10
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:08:57.212672Z",
          "start_time": "2025-01-17T06:08:57.209789Z"
        },
        "id": "oCFKqMl3ezzQ"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "@torch.no_grad()\n",
        "def evaluating(model, dataloader, loss_fct):\n",
        "    loss_list = []\n",
        "    for datas, labels in dataloader:\n",
        "        datas = datas.to(device)\n",
        "        labels = labels.to(device)\n",
        "        # 前向计算\n",
        "        logits = model(datas)\n",
        "        loss = loss_fct(logits, labels)         # 验证集损失\n",
        "        loss_list.append(loss.item())\n",
        "\n",
        "    return np.mean(loss_list)\n"
      ],
      "outputs": [],
      "execution_count": 11
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:09:08.508057Z",
          "start_time": "2025-01-17T06:09:08.503798Z"
        },
        "id": "6NvKxvAvezzR"
      },
      "source": [
        "# 训练\n",
        "def training(\n",
        "    model,\n",
        "    train_loader,\n",
        "    val_loader,\n",
        "    epoch,\n",
        "    loss_fct,\n",
        "    optimizer,\n",
        "    tensorboard_callback=None,\n",
        "    save_ckpt_callback=None,\n",
        "    early_stop_callback=None,\n",
        "    eval_step=500,\n",
        "    ):\n",
        "    record_dict = {\n",
        "        \"train\": [],\n",
        "        \"val\": []\n",
        "    }\n",
        "\n",
        "    global_step = 0\n",
        "    model.train()\n",
        "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
        "        for epoch_id in range(epoch):\n",
        "            # training\n",
        "            for datas, labels in train_loader:\n",
        "                datas = datas.to(device)\n",
        "                labels = labels.to(device)\n",
        "                # 梯度清空\n",
        "                optimizer.zero_grad()\n",
        "                # 模型前向计算\n",
        "                logits = model(datas)\n",
        "                # 计算损失\n",
        "                loss = loss_fct(logits, labels)\n",
        "                # 梯度回传\n",
        "                loss.backward()\n",
        "                # 调整优化器，包括学习率的变动等\n",
        "                optimizer.step()\n",
        "\n",
        "                loss = loss.cpu().item()\n",
        "                # record\n",
        "\n",
        "                record_dict[\"train\"].append({\n",
        "                    \"loss\": loss, \"step\": global_step\n",
        "                })\n",
        "\n",
        "                # evaluating\n",
        "                if global_step % eval_step == 0:\n",
        "                    model.eval()\n",
        "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
        "                    record_dict[\"val\"].append({\n",
        "                        \"loss\": val_loss, \"step\": global_step\n",
        "                    })\n",
        "                    model.train()\n",
        "\n",
        "                    # 早停 Early Stop\n",
        "                    if early_stop_callback is not None:\n",
        "                        early_stop_callback(-val_loss)\n",
        "                        if early_stop_callback.early_stop:\n",
        "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
        "                            return record_dict\n",
        "\n",
        "                # udate step\n",
        "                global_step += 1\n",
        "                pbar.update(1)\n",
        "                pbar.set_postfix({\"epoch\": epoch_id})\n",
        "\n",
        "    return record_dict\n"
      ],
      "outputs": [],
      "execution_count": 12
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:09:15.755957Z",
          "start_time": "2025-01-17T06:09:15.752945Z"
        },
        "id": "PecLh6xrezzS"
      },
      "source": [
        "#画线要注意的是损失是不一定在零到1之间的\n",
        "def plot_learning_curves(record_dict, sample_step=5):\n",
        "    # build DataFrame\n",
        "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
        "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
        "\n",
        "    # plot\n",
        "    for idx, item in enumerate(train_df.columns):\n",
        "        plt.plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
        "        plt.plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
        "        plt.grid()\n",
        "        plt.legend()\n",
        "        # plt.xticks(range(0, train_df.index[-1], 10*sample_step), range(0, train_df.index[-1], 10*sample_step))\n",
        "        plt.xlabel(\"step\")\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "# plot_learning_curves(record)  #横坐标是 steps"
      ],
      "outputs": [],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhXT-YnxezzS"
      },
      "source": [
        "# 网格搜索，for循环实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-01-17T06:17:45.512951Z",
          "start_time": "2025-01-17T06:17:11.768175Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "f4df7f9b7a7b48908c863256072a0b51",
            "274dc705d2d740788a8ed18d6166cc65",
            "f9fd558ca1e84aec89680559a48fe1f4",
            "ae7cee751a3149bda2dd6a1d6aadca22",
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            "b2b730114d1d42b6b3136683e586b35b",
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            "ed0218c825f44c95982c540a5648aaf5",
            "2b3cfb46a88b4c77a520823da90843f0",
            "f5ef8748e4be4135aa0b9f80bad7c74a",
            "c062a46eb7c64284a3b133348b76b907",
            "873d13cee7cf41cdbbfa39f2fb91a1dd",
            "cd922c1dd64940edb7a00bb888666255",
            "b85e4caadea34641b414f043ee562e47",
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            "91a42e6872ec434bb3ce742c455180dc",
            "7e1717e0b6914236aeadbba90fc9c6d1",
            "54ebb95f534b4adbb0c792a7135a2647",
            "164db7fefbf848758c4e6152aec430a2",
            "ba4da8e9edc64470a9ea074d7c0613bf",
            "c284de9402704c7eb6b7d3c17cdc3796",
            "c747cca5499d42a0a1b3a69a45765ef0",
            "6e2809285c4e402083da263a282bd6d8"
          ]
        },
        "id": "9HfSGQ4BezzT",
        "outputId": "0481c438-9a74-4319-bd6f-8e8f19e220c1"
      },
      "source": [
        "for lr in [1e-2, 3e-2, 3e-1, 1e-3]:\n",
        "\n",
        "    epoch = 100\n",
        "\n",
        "    model = NeuralNetwork()\n",
        "\n",
        "    # 1. 定义损失函数 采用MSE损失\n",
        "    loss_fct = nn.MSELoss()\n",
        "    # 2. 定义优化器 采用SGD\n",
        "    # Optimizers specified in the torch.optim package\n",
        "    optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)\n",
        "\n",
        "    # 3. early stop\n",
        "    early_stop_callback = EarlyStopCallback(patience=10, min_delta=1e-3)\n",
        "\n",
        "    model = model.to(device)\n",
        "    record = training(\n",
        "        model,\n",
        "        train_loader,\n",
        "        val_loader,\n",
        "        epoch,\n",
        "        loss_fct,\n",
        "        optimizer,\n",
        "        early_stop_callback=early_stop_callback,\n",
        "        eval_step=len(train_loader)\n",
        "        )\n",
        "    print(\"lr: {}\".format(lr))\n",
        "    plot_learning_curves(record)\n",
        "    model.eval()\n",
        "    loss = evaluating(model, val_loader, loss_fct)\n",
        "    print(f\"loss:     {loss:.4f}\")"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4df7f9b7a7b48908c863256072a0b51"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stop at epoch 68 / global_step 3128\n",
            "lr: 0.01\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss:     0.3323\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de3e37d67575490f8a5feeff5f7f7c06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stop at epoch 53 / global_step 2438\n",
            "lr: 0.03\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss:     0.3473\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce1f9606fb0246b2940c35d3f6f8b9e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stop at epoch 9 / global_step 414\n",
            "lr: 0.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss:     nan\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b85e4caadea34641b414f043ee562e47"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "lr: 0.001\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss:     0.3819\n"
          ]
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5SipBeq5f5pA"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU",
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